/*================================================================*
 * Filename  bpnet.h                                              *
 * Description:                                                   *
 * 一個微型神經網絡，使用BP來調整權值                             *
 *================================================================*/

#include <math.h>
#include <stdlib.h>
#include <time.h>

#define BP_LEARNING  (float)(0.5)   //學習系數

class CBPNet
{
public:
	CBPNet();
	~CBPNet();
	float Train(float,float,float);
	float Run(float,float);
	
protected:
private:
	float m_fWeights[3][3];  //三個神經元
	float Sigmoid(float);    //S形函數
};

CBPNet::CBPNet()
{
	srand((unsigned)(time(NULL)));

	for(int i=0;i<3;i++)
	{
		for(int j=0;j<3;j++)
		{
			//由于一些原因，微軟提供的rand()函數產生的隨機數是
			//整數，所以需要將之做適當的變換，變為－1～1之間
			m_fWeights[i][j]=(float)(rand())/(32767/2)-1;
		}
	}
}

CBPNet::~CBPNet()
{
}

//訓練網絡

float CBPNet::Train(float i1,float i2,float d)
{
	//變量
	float net1, net2, i3, i4, out;

	//計算隱含層的神經元值
	net1 = 1 * m_fWeights[0][0] + i1 * m_fWeights[1][0] +
		    i2 * m_fWeights[2][0];
	net2 = 1 * m_fWeights[0][1] + i1 * m_fWeights[1][1] +
             i2 * m_fWeights[2][1];
	
	//使用S函數
	i3 = Sigmoid(net1);
	i4 = Sigmoid(net2);

	//計算輸出層的值
	net1 = 1 * m_fWeights[0][2] + i3 * m_fWeights[1][2] +
		    i4 * m_fWeights[2][2];
	out = Sigmoid(net1);
	
	//計算誤差，反向傳播
	float deltas[3];

	deltas[2] = out*(1-out)*(d-out);
	deltas[1] = i4*(1-i4)*(m_fWeights[2][2])*(deltas[2]);
	deltas[0] = i3*(1-i3)*(m_fWeights[1][2])*(deltas[2]);
	
	//調整權值
	float v1=i1, v2=i2;
	for(int i=0; i<3; i++)
	{
		//如果有必要，改變輸出層的值
		if (i==2)
		{
			v1=i3;
			v2=i4;
		}
		m_fWeights[0][i]+=BP_LEARNING*1*deltas[i];
		m_fWeights[1][i]+=BP_LEARNING*v1*deltas[i];
		m_fWeights[2][i]+=BP_LEARNING*v2*deltas[i];
	}
	return out;
}


//s函數
float CBPNet::Sigmoid(float num)
{
	return (float)(1/(1+exp(-num)));
}

//運行網絡
float CBPNet::Run(float i1,float i2)
{
	float net1,net2,i3,i4;
	net1 = 1 * m_fWeights[0][0] + i1 * m_fWeights[1][0] +
		i2 * m_fWeights[2][0];
	net2 = 1 * m_fWeights[0][1] + i1 * m_fWeights[1][1] +
		i2 * m_fWeights[2][1];
	i3 = Sigmoid(net1);
	i4 = Sigmoid(net2);
	net1 = 1 * m_fWeights[0][2] + i3 * m_fWeights[1][2] +
		    i4 * m_fWeights[2][2];
	return Sigmoid(net1);
}